SPENDING PREDICTION MODEL¶

In [3]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
In [4]:
Tz=pd.read_csv('Train .csv')
In [5]:
Tz
Out[5]:
ID country age_group travel_with total_female total_male purpose main_activity info_source tour_arrangement ... package_transport_tz package_sightseeing package_guided_tour package_insurance night_mainland night_zanzibar payment_mode first_trip_tz most_impressing total_cost
0 tour_0 SWIZERLAND 45-64 Friends/Relatives 1.0 1.0 Leisure and Holidays Wildlife tourism Friends, relatives Independent ... No No No No 13.0 0.0 Cash No Friendly People 674602.5
1 tour_10 UNITED KINGDOM 25-44 NaN 1.0 0.0 Leisure and Holidays Cultural tourism others Independent ... No No No No 14.0 7.0 Cash Yes Wonderful Country, Landscape, Nature 3214906.5
2 tour_1000 UNITED KINGDOM 25-44 Alone 0.0 1.0 Visiting Friends and Relatives Cultural tourism Friends, relatives Independent ... No No No No 1.0 31.0 Cash No Excellent Experience 3315000.0
3 tour_1002 UNITED KINGDOM 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes No 11.0 0.0 Cash Yes Friendly People 7790250.0
4 tour_1004 CHINA 1-24 NaN 1.0 0.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Independent ... No No No No 7.0 4.0 Cash Yes No comments 1657500.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
4804 tour_993 UAE 45-64 Alone 0.0 1.0 Business Hunting tourism Friends, relatives Independent ... No No No No 2.0 0.0 Credit Card No No comments 3315000.0
4805 tour_994 UNITED STATES OF AMERICA 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes Yes 11.0 0.0 Cash Yes Friendly People 10690875.0
4806 tour_995 NETHERLANDS 1-24 NaN 1.0 0.0 Leisure and Holidays Wildlife tourism others Independent ... No No No No 3.0 7.0 Cash Yes Good service 2246636.7
4807 tour_997 SOUTH AFRICA 25-44 Friends/Relatives 1.0 1.0 Business Beach tourism Travel, agent, tour operator Independent ... No No No No 5.0 0.0 Credit Card No Friendly People 1160250.0
4808 tour_999 UNITED KINGDOM 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes No 4.0 7.0 Cash Yes Friendly People 13260000.0

4809 rows × 23 columns

In [6]:
Tz.head(20)
Out[6]:
ID country age_group travel_with total_female total_male purpose main_activity info_source tour_arrangement ... package_transport_tz package_sightseeing package_guided_tour package_insurance night_mainland night_zanzibar payment_mode first_trip_tz most_impressing total_cost
0 tour_0 SWIZERLAND 45-64 Friends/Relatives 1.0 1.0 Leisure and Holidays Wildlife tourism Friends, relatives Independent ... No No No No 13.0 0.0 Cash No Friendly People 674602.5
1 tour_10 UNITED KINGDOM 25-44 NaN 1.0 0.0 Leisure and Holidays Cultural tourism others Independent ... No No No No 14.0 7.0 Cash Yes Wonderful Country, Landscape, Nature 3214906.5
2 tour_1000 UNITED KINGDOM 25-44 Alone 0.0 1.0 Visiting Friends and Relatives Cultural tourism Friends, relatives Independent ... No No No No 1.0 31.0 Cash No Excellent Experience 3315000.0
3 tour_1002 UNITED KINGDOM 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes No 11.0 0.0 Cash Yes Friendly People 7790250.0
4 tour_1004 CHINA 1-24 NaN 1.0 0.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Independent ... No No No No 7.0 4.0 Cash Yes No comments 1657500.0
5 tour_1005 UNITED KINGDOM 25-44 NaN 0.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... No Yes Yes No 9.0 3.0 Cash Yes Wildlife 120950.0
6 tour_1007 SOUTH AFRICA 45-64 Alone 0.0 1.0 Business Mountain climbing Friends, relatives Independent ... No No No No 9.0 0.0 Cash Yes Friendly People 466140.0
7 tour_1008 UNITED STATES OF AMERICA 45-64 Friends/Relatives 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes Yes 10.0 3.0 Cash Yes Friendly People 3480750.0
8 tour_101 NIGERIA 25-44 Alone 0.0 1.0 Leisure and Holidays Cultural tourism Travel, agent, tour operator Independent ... No No No No 4.0 0.0 Cash Yes NaN 994500.0
9 tour_1011 INDIA 25-44 Alone 1.0 0.0 Business Wildlife tourism Travel, agent, tour operator Independent ... No No No No 5.0 0.0 Credit Card Yes Friendly People 2486250.0
10 tour_1012 BRAZIL 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Radio, TV, Web Independent ... No No No No 17.0 3.0 Cash Yes Wonderful Country, Landscape, Nature 1117155.0
11 tour_1013 CANADA 45-64 Children 2.0 0.0 Leisure and Holidays Beach tourism Friends, relatives Independent ... No No No No 30.0 0.0 Cash No Excellent Experience 8121750.0
12 tour_1016 CANADA 45-64 Children 0.0 2.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Independent ... No No No No 11.0 3.0 Cash Yes No comments 331500.0
13 tour_1017 MALT 25-44 Friends/Relatives 2.0 0.0 Leisure and Holidays Wildlife tourism Friends, relatives Package Tour ... Yes No No No 10.0 0.0 Cash Yes No comments 11346650.0
14 tour_1018 MOZAMBIQUE 25-44 Alone 0.0 1.0 Visiting Friends and Relatives Beach tourism Friends, relatives Independent ... No No No No 2.0 0.0 Cash Yes Wildlife 497250.0
15 tour_102 RWANDA 65+ Alone 1.0 0.0 Leisure and Holidays Beach tourism Friends, relatives Independent ... No No No No 0.0 2.0 Cash Yes Wonderful Country, Landscape, Nature 331500.0
16 tour_1021 AUSTRIA 45-64 Friends/Relatives 4.0 1.0 Visiting Friends and Relatives Mountain climbing Friends, relatives Independent ... No No No No 24.0 0.0 Cash No Friendly People 2000000.0
17 tour_1022 MYANMAR 25-44 NaN 1.0 0.0 Meetings and Conference Wildlife tourism Radio, TV, Web Independent ... No No No No 5.0 0.0 Cash Yes Friendly People 331500.0
18 tour_1024 GERMANY 25-44 Children 1.0 1.0 Visiting Friends and Relatives Cultural tourism Friends, relatives Independent ... No No No No 3.0 0.0 Cash Yes Friendly People 2269330.0
19 tour_1026 KENYA 25-44 NaN 1.0 0.0 Business Mountain climbing Friends, relatives Independent ... No No No No 4.0 0.0 Cash No Friendly People 377520.0

20 rows × 23 columns

In [7]:
Tz.describe()
Out[7]:
total_female total_male night_mainland night_zanzibar total_cost
count 4806.000000 4804.000000 4809.000000 4809.000000 4.809000e+03
mean 0.926758 1.009575 8.488043 2.304429 8.114389e+06
std 1.288242 1.138865 10.427624 4.227080 1.222490e+07
min 0.000000 0.000000 0.000000 0.000000 4.900000e+04
25% 0.000000 1.000000 3.000000 0.000000 8.121750e+05
50% 1.000000 1.000000 6.000000 0.000000 3.397875e+06
75% 1.000000 1.000000 11.000000 4.000000 9.945000e+06
max 49.000000 44.000000 145.000000 61.000000 9.953288e+07
In [8]:
Tz.isnull().sum()
Out[8]:
ID                          0
country                     0
age_group                   0
travel_with              1114
total_female                3
total_male                  5
purpose                     0
main_activity               0
info_source                 0
tour_arrangement            0
package_transport_int       0
package_accomodation        0
package_food                0
package_transport_tz        0
package_sightseeing         0
package_guided_tour         0
package_insurance           0
night_mainland              0
night_zanzibar              0
payment_mode                0
first_trip_tz               0
most_impressing           313
total_cost                  0
dtype: int64
In [9]:
Tz['travel_with'].fillna(value='Alone', inplace=True)
In [10]:
Tz['most_impressing'].fillna(value='friendly people', inplace=True)
In [11]:
Tz['total_male'].fillna(method='bfill', inplace=True)
In [12]:
Tz['total_female'].fillna(method='bfill', inplace=True)
In [13]:
Tz.isnull().sum()
Out[13]:
ID                       0
country                  0
age_group                0
travel_with              0
total_female             0
total_male               0
purpose                  0
main_activity            0
info_source              0
tour_arrangement         0
package_transport_int    0
package_accomodation     0
package_food             0
package_transport_tz     0
package_sightseeing      0
package_guided_tour      0
package_insurance        0
night_mainland           0
night_zanzibar           0
payment_mode             0
first_trip_tz            0
most_impressing          0
total_cost               0
dtype: int64

EXPLORATORY DATA ANALYSIS¶

In [14]:
Tz.shape
Out[14]:
(4809, 23)
In [15]:
Tz.drop_duplicates()
Out[15]:
ID country age_group travel_with total_female total_male purpose main_activity info_source tour_arrangement ... package_transport_tz package_sightseeing package_guided_tour package_insurance night_mainland night_zanzibar payment_mode first_trip_tz most_impressing total_cost
0 tour_0 SWIZERLAND 45-64 Friends/Relatives 1.0 1.0 Leisure and Holidays Wildlife tourism Friends, relatives Independent ... No No No No 13.0 0.0 Cash No Friendly People 674602.5
1 tour_10 UNITED KINGDOM 25-44 Alone 1.0 0.0 Leisure and Holidays Cultural tourism others Independent ... No No No No 14.0 7.0 Cash Yes Wonderful Country, Landscape, Nature 3214906.5
2 tour_1000 UNITED KINGDOM 25-44 Alone 0.0 1.0 Visiting Friends and Relatives Cultural tourism Friends, relatives Independent ... No No No No 1.0 31.0 Cash No Excellent Experience 3315000.0
3 tour_1002 UNITED KINGDOM 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes No 11.0 0.0 Cash Yes Friendly People 7790250.0
4 tour_1004 CHINA 1-24 Alone 1.0 0.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Independent ... No No No No 7.0 4.0 Cash Yes No comments 1657500.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
4804 tour_993 UAE 45-64 Alone 0.0 1.0 Business Hunting tourism Friends, relatives Independent ... No No No No 2.0 0.0 Credit Card No No comments 3315000.0
4805 tour_994 UNITED STATES OF AMERICA 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes Yes 11.0 0.0 Cash Yes Friendly People 10690875.0
4806 tour_995 NETHERLANDS 1-24 Alone 1.0 0.0 Leisure and Holidays Wildlife tourism others Independent ... No No No No 3.0 7.0 Cash Yes Good service 2246636.7
4807 tour_997 SOUTH AFRICA 25-44 Friends/Relatives 1.0 1.0 Business Beach tourism Travel, agent, tour operator Independent ... No No No No 5.0 0.0 Credit Card No Friendly People 1160250.0
4808 tour_999 UNITED KINGDOM 25-44 Spouse 1.0 1.0 Leisure and Holidays Wildlife tourism Travel, agent, tour operator Package Tour ... Yes Yes Yes No 4.0 7.0 Cash Yes Friendly People 13260000.0

4809 rows × 23 columns

In [16]:
Tz.shape
Out[16]:
(4809, 23)
In [17]:
Tz.describe()
Out[17]:
total_female total_male night_mainland night_zanzibar total_cost
count 4809.000000 4809.000000 4809.000000 4809.000000 4.809000e+03
mean 0.927012 1.009773 8.488043 2.304429 8.114389e+06
std 1.288257 1.139276 10.427624 4.227080 1.222490e+07
min 0.000000 0.000000 0.000000 0.000000 4.900000e+04
25% 0.000000 1.000000 3.000000 0.000000 8.121750e+05
50% 1.000000 1.000000 6.000000 0.000000 3.397875e+06
75% 1.000000 1.000000 11.000000 4.000000 9.945000e+06
max 49.000000 44.000000 145.000000 61.000000 9.953288e+07
In [18]:
Tz['country'].value_counts()
Out[18]:
UNITED STATES OF AMERICA    695
UNITED KINGDOM              533
ITALY                       393
FRANCE                      280
ZIMBABWE                    274
                           ... 
NIGER                         1
MYANMAR                       1
DOMINICA                      1
TUNISIA                       1
ESTONIA                       1
Name: country, Length: 105, dtype: int64
In [19]:
Tz['payment_mode'].value_counts()
Out[19]:
Cash                 4172
Credit Card           622
Other                   8
Travellers Cheque       7
Name: payment_mode, dtype: int64
In [20]:
Tz['travel_with'].value_counts()
Out[20]:
Alone                  2379
Spouse                 1005
Friends/Relatives       895
Spouse and Children     368
Children                162
Name: travel_with, dtype: int64
In [21]:
Tz['purpose'].value_counts()
Out[21]:
Leisure and Holidays              2840
Business                           671
Visiting Friends and Relatives     633
Meetings and Conference            312
Volunteering                       138
Other                              128
Scientific and Academic             87
Name: purpose, dtype: int64
In [22]:
Tz['most_impressing'].value_counts()
Out[22]:
Friendly People                         1541
 Wildlife                               1038
No comments                              743
Wonderful Country, Landscape, Nature     507
Good service                             365
friendly people                          313
Excellent Experience                     271
Satisfies and Hope Come Back              31
Name: most_impressing, dtype: int64
In [23]:
sns.pairplot(Tz)
Out[23]:
<seaborn.axisgrid.PairGrid at 0x25b2bb913d0>
In [24]:
pip install pandas-profiling

Requirement already satisfied: pandas-profiling in c:\users\latitude e7470\anaconda3\lib\site-packages (3.3.0)
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Note: you may need to restart the kernel to use updated packages.
In [25]:
from pandas_profiling import ProfileReport
profile=ProfileReport(Tz)
profile

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]
Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]
Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]
Out[25]:
























In [26]:



     


sns.catplot(data=Tz, x='payment_mode', kind='count')



     






















    
    
Out[26]:








<seaborn.axisgrid.FacetGrid at 0x25b33251eb0>










    
    































In [27]:



     


sns.catplot(data=Tz, y='main_activity', kind='count')



     






















    
    
Out[27]:








<seaborn.axisgrid.FacetGrid at 0x25b405a6520>










    
    































In [28]:



     


country=Tz[['country', 'total_cost']].head(8).groupby('country').sum()



     



















In [29]:



     


country



     






















    
    
Out[29]:











  
    

      
      total_cost
    

    

      country
      
    

  
  
    

      CHINA
      1657500.0
    

    

      SOUTH AFRICA
      466140.0
    

    

      SWIZERLAND
      674602.5
    

    

      UNITED KINGDOM
      14441106.5
    

    

      UNITED STATES OF AMERICA
      3480750.0
    

  



























In [30]:



     


Age=Tz[['age_group', 'total_cost', 'travel_with']]



     



















In [31]:



     


Age



     






















    
    
Out[31]:











  
    

      
      age_group
      total_cost
      travel_with
    

  
  
    

      0
      45-64
      674602.5
      Friends/Relatives
    

    

      1
      25-44
      3214906.5
      Alone
    

    

      2
      25-44
      3315000.0
      Alone
    

    

      3
      25-44
      7790250.0
      Spouse
    

    

      4
      1-24
      1657500.0
      Alone
    

    

      ...
      ...
      ...
      ...
    

    

      4804
      45-64
      3315000.0
      Alone
    

    

      4805
      25-44
      10690875.0
      Spouse
    

    

      4806
      1-24
      2246636.7
      Alone
    

    

      4807
      25-44
      1160250.0
      Friends/Relatives
    

    

      4808
      25-44
      13260000.0
      Spouse
    

  



4809 rows × 3 columns


























In [32]:



     


Age.groupby('age_group').sum()



     






















    
    
Out[32]:











  
    

      
      total_cost
    

    

      age_group
      
    

  
  
    

      1-24
      3.379088e+09
    

    

      25-44
      1.498710e+10
    

    

      45-64
      1.537184e+10
    

    

      65+
      5.284068e+09
    

  



























In [33]:



     


Age[['age_group']].value_counts()



     






















    
    
Out[33]:








age_group
25-44        2487
45-64        1391
1-24          624
65+           307
dtype: int64
























In [34]:



     


Age[['age_group']].value_counts().plot(kind='pie')



     






















    
    
Out[34]:








<AxesSubplot:ylabel='None'>










    
    































In [35]:



     


Tz[['night_mainland']].mean()



     






















    
    
Out[35]:








night_mainland    8.488043
dtype: float64
























In [36]:



     


Tz[['night_zanzibar']].mean()



     






















    
    
Out[36]:








night_zanzibar    2.304429
dtype: float64
























In [37]:



     


sns.distplot(Tz['total_cost']).set(title='SPENDING DISTRIBUTION')



     






















    
    






C:\Users\Latitude E7470\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)










    
    
Out[37]:








[Text(0.5, 1.0, 'SPENDING DISTRIBUTION')]










    
    































In [38]:



     


import warnings
warnings.filterwarnings('ignore')



     



















In [39]:



     


Tz.corr()



     






















    
    
Out[39]:











  
    

      
      total_female
      total_male
      night_mainland
      night_zanzibar
      total_cost
    

  
  
    

      total_female
      1.000000
      0.467000
      0.031233
      0.138523
      0.285862
    

    

      total_male
      0.467000
      1.000000
      -0.041369
      0.050172
      0.183785
    

    

      night_mainland
      0.031233
      -0.041369
      1.000000
      -0.118155
      0.020473
    

    

      night_zanzibar
      0.138523
      0.050172
      -0.118155
      1.000000
      0.145139
    

    

      total_cost
      0.285862
      0.183785
      0.020473
      0.145139
      1.000000
    

  



























In [40]:



     


sns.heatmap(Tz.corr(), annot=True)



     






















    
    
Out[40]:








<AxesSubplot:>










    
    



































Future engeneering¶


















In [41]:



     


Tz.columns



     






















    
    
Out[41]:








Index(['ID', 'country', 'age_group', 'travel_with', 'total_female',
       'total_male', 'purpose', 'main_activity', 'info_source',
       'tour_arrangement', 'package_transport_int', 'package_accomodation',
       'package_food', 'package_transport_tz', 'package_sightseeing',
       'package_guided_tour', 'package_insurance', 'night_mainland',
       'night_zanzibar', 'payment_mode', 'first_trip_tz', 'most_impressing',
       'total_cost'],
      dtype='object')




























PREPARING DATA FOR MODELLING¶


















In [42]:



     


#target varieble or prediction variable
x=Tz[['total_female',
       'total_male','night_mainland',
       'night_zanzibar',]]



     



















In [43]:



     


y=Tz['total_cost']



     



















In [44]:



     


#train, test, split.
from sklearn.model_selection import train_test_split



     



















In [63]:



     


x_test, x_train, y_test, y_train=train_test_split(x,y,test_size=0.9,random_state=60)



     























BUILDING A REGRESSION MODEL¶


















In [64]:



     


#CREATING AND TRAINING A MODEL
from sklearn.linear_model import LinearRegression



     



















In [65]:



     


#instantiate model
lm = LinearRegression()



     



















In [66]:



     


 lm.fit(x_train, y_train)



     






















    
    
Out[66]:








LinearRegression()
























In [67]:



     


print(lm.intercept_)



     






















    
    






4462382.634319042




























lm.coef_



















In [68]:



     


x_train.columns



     






















    
    
Out[68]:








Index(['total_female', 'total_male', 'night_mainland', 'night_zanzibar'], dtype='object')
























In [69]:



     


Tz2 = pd.DataFrame(lm.coef_, x_train.columns, columns=['total_spendings'])



     



















In [70]:



     


Tz2



     






















    
    
Out[70]:











  
    

      
      total_spendings
    

  
  
    

      total_female
      2.071454e+06
    

    

      total_male
      7.488670e+05
    

    

      night_mainland
      3.555253e+04
    

    

      night_zanzibar
      2.930572e+05
    

  































MODEL EVALUATION¶


















In [71]:



     


#evaluating our model performance on spending behaviour
prediction=lm.predict(x_test)



     



















In [72]:



     


#predicted spending cost of tourists/indicating their spending behaviour
prediction



     






















    
    
Out[72]:








array([ 5317907.18511913,  5602327.3897838 ,  9334103.93746147,
        5708984.96653305,  5389012.23628529,  5957852.64561464,
        5246802.13395296,  6676046.37885942,  5424564.76186838,
        5246802.13395296,  6102326.68475304,  9582971.61654306,
       12636660.34162493,  5282354.65953604,  7389360.82732717,
       10458549.12266763,  5460117.28745146,  6410394.06156772,
        8778173.26187351,  5353459.71070221,  8458200.53162576,
        5424564.76186838,  9582482.18211578,  5246802.13395296,
        5531222.33861763, 10799390.14237829,  7496018.40407642,
        9334103.93746147,  5682069.47726866,  5744537.49211613,
        8988220.09825763,  6137879.21033613,  7780438.60874109,
        6960466.58352409,  9103514.71132558, 10600759.22499997,
        5246802.13395296, 10316339.0203353 , 13175806.37900278,
        7531570.92965951,  5566774.86420071, 11130778.79163185,
        9334103.93746147,  6552604.16390006,  6604941.32769325,
        5353459.71070221,  7389360.82732717,  6640493.85327634,
       21589473.88347938, 13477011.22187756,  5389012.23628529,
        8410976.91084743,  5389012.23628529,  6604941.32769325,
        9041046.69647811,  5317907.18511913,  5460117.28745146,
       10924326.17207324,  9716544.6825567 ,  5282354.65953604,
        5282354.65953604,  6457851.94058388,  7067124.16027335,
        7531570.92965951,  6747151.43002559, 11954345.03367437,
        8703799.89359296,  7460465.87849334, 11385504.62434502,
        7496018.40407642, 12596064.99654867, 15850674.08726869,
        7709333.55757493,  8529305.58279193,  7031571.63469026,
       10133533.5729267 ,  6953323.36194099, 14173051.59770792,
        8846009.9959253 ,  6277825.37586239,  9760734.24445848,
        7673781.03199184, 10396081.10782016,  5282354.65953604,
        6676046.37885942,  7531570.92965951,  5246802.13395296,
        5424564.76186838,  5246802.13395296,  9041046.69647811,
        5460117.28745146, 20337828.7317722 ,  7884480.83751938,
        9260734.94948923,  5460117.28745146, 11888282.80200137,
        9674129.62303372,  5317907.18511913,  5566774.86420071,
        9531919.52070138,  5282354.65953604,  6031221.63358687,
        6711598.9044425 , 10799390.14237829, 10890058.71444168,
       11903292.93783269, 11982545.59089027,  6676046.37885942,
        5460117.28745146,  6889361.53235793,  5282354.65953604,
        5282354.65953604,  7166638.51543949,  7546581.06549082,
        7531570.92965951,  5708984.96653305,  6711598.9044425 ,
        7638228.50640876,  6889361.53235793,  7602675.98082567,
        5708984.96653305,  7638228.50640876,  9911091.94868223,
       17719081.45029489,  7353808.30174408,  6640493.85327634,
        9103025.2768983 ,  7351544.36493802,  6640493.85327634,
        6889361.53235793,  9929018.99046303,  9103025.2768983 ,
        8668247.36800988, 10411091.24365147,  6604941.32769325,
       11991672.06163624, 12048771.3572794 ,  7031571.63469026,
        7839638.30647419,  7673781.03199184,  5317907.18511913,
        5744537.49211613,  9654076.66770923,  5282354.65953604,
        9496856.42954557,  6339289.01040156,  5282354.65953604,
        8061590.49629138,  6171167.79911314,  5424564.76186838,
        4960117.99248222,  6031221.63358687,  5317907.18511913,
       13915546.88230764,  6815641.13322079,  8363284.77359344,
        8996857.13457633,  8747989.45549474,  6161526.38248614,
        7496018.40407642,  7673781.03199184, 12110234.99181857,
        5460117.28745146,  7709333.55757493,  7067124.16027335,
       12098493.17310163,  8141332.58377624,  7493754.46727035,
        5353459.71070221,  5389012.23628529, 11567820.63732634,
        5424564.76186838,  5922300.12003155,  7262650.29525343,
        7325118.3101009 ,  6117336.82058436,  8454932.21451138,
        6315641.83825155,  5317907.18511913, 11231063.26886847,
        7875190.83205727,  7318255.776161  ,  9041046.69647811,
        6747151.43002559, 13021854.45795351,  8996857.13457633,
        9103025.2768983 ,  5317907.18511913,  7638228.50640876,
        7353808.30174408,  5708984.96653305,  8668247.36800988,
        5353459.71070221,  6811113.25960865,  5353459.71070221,
        8961304.60899324, 13095574.85709064,  5353459.71070221,
        5957852.64561464,  5424564.76186838, 18117653.86445682,
        6640493.85327634,  5424564.76186838,  6676046.37885942,
        5815642.5432823 ,  7262650.29525343,  6640493.85327634,
        8280437.90371034, 11231063.26886847,  5282354.65953604,
        9334103.93746147,  9135962.45451042,  7460465.87849334,
        6640493.85327634,  5531222.33861763,  7531570.92965951,
        6102326.68475304,  5246802.13395296,  6782703.95560867,
        9733563.57900465,  5424564.76186838,  6330651.97408286,
        5353459.71070221,  7031571.63469026,  5637879.91536688,
       11226020.4493753 ,  7582133.59107391, 10625130.08981509,
        6889361.53235793,  6782703.95560867, 13291615.93795175,
        7274555.6486865 , 20597622.89543231,  6359831.40015332,
        7600412.04401961,  5531222.33861763,  6517051.63831698,
        7244886.78818876,  9031920.22573214,  9254361.84997661,
        5389012.23628529,  5282354.65953604,  5246802.13395296,
        5317907.18511913,  5637879.91536688,  7280439.31377185,
        6960466.58352409, 12442113.0754994 ,  5353459.71070221,
        7280439.31377185,  5424564.76186838,  6640493.85327634,
        7638228.50640876,  6711598.9044425 ,  6223994.39733361,
        7324628.87567363, 10082970.9115123 , 11678561.86532839,
        5646516.95168557, 11781625.22525212,  5353459.71070221,
        8176885.10935933,  9361019.42672586,  6028957.69678081,
        5424564.76186838,  7424913.35291025,  7999122.48144391,
        6552604.16390006,  9022513.06734296,  5246802.13395296,
       12101597.95549988,  5353459.71070221,  9103025.2768983 ,
       10538780.64457977,  7575760.49156128,  9582971.61654306,
        6351194.36383463,  9547419.09095997,  5993405.17119772,
        5317907.18511913,  6398488.70813465, 15048955.00354363,
        8351542.95487651,  8659610.33169119,  7424913.35291025,
        6315641.83825155,  9041046.69647811,  7389360.82732717,
        5246802.13395296,  5353459.71070221,  5317907.18511913,
        5389012.23628529, 15317875.63794971, 10280786.49475221,
        7111803.1566024 ,  5602327.3897838 ,  7600412.04401961,
       12953854.18918558,  5424564.76186838,  6604941.32769325,
        9334103.93746147,  7496018.40407642,  8747989.45549474,
        5282354.65953604,  9183256.79881044, 10568311.48181513,
       10861368.72279849,  7496018.40407642,  7111803.1566024 ,
        8561589.79126063, 14462351.08714963,  7839638.30647419,
        9334103.93746147,  7699926.39918575,  7496018.40407642,
        5211249.60836987, 11421057.14992811,  5460117.28745146,
       10494591.08267799,  5353459.71070221,  8996857.13457633,
        8339637.60144343,  5708984.96653305,  6410394.06156772,
        6493404.46616697,  7744886.08315801,  5531222.33861763,
        7531570.92965951,  7460465.87849334,  6889361.53235793,
        5317907.18511913,  8161874.97352801,  7887096.18549035,
        7531570.92965951,  6960466.58352409,  9289914.37555969,
        7709333.55757493,  6173431.73591921,  7280439.31377185,
        7460465.87849334,  5753174.52843483,  6925403.49236828,
        9334103.93746147,  9538782.05464128, 14133271.58677007,
       12387792.66254334,  7661875.67855877,  9103514.71132558,
        5282354.65953604, 12154424.55372034,  5317907.18511913,
        6747151.43002559,  5531222.33861763,  5282354.65953604,
        8454932.21451138,  8363284.77359344,  9887444.77653222,
        5317907.18511913,  7709333.55757493,  6676046.37885942,
        9662224.26960064,  7602675.98082567, 21706894.41009102,
        6386746.88941771,  7460465.87849334,  7344401.14335491,
        8834104.64249223,  8161874.97352801,  8541047.40150886,
        5780090.01769922,  8668247.36800988,  5566774.86420071,
        9281277.339241  ,  5995669.10800379,  5353459.71070221,
        9334103.93746147,  6031221.63358687,  6066774.15916996,
        8810457.47034222,  8925752.08341016, 11678561.86532839,
        7999122.48144391,  7531570.92965951,  8339637.60144343,
        5708984.96653305,  6173431.73591921,  6604941.32769325,
        8349279.01807044,  8461305.314024  ,  5637879.91536688,
        7839638.30647419,  5531222.33861763,  5246802.13395296,
        5282354.65953604,  6640493.85327634,  6676046.37885942,
       11195510.74328539,  6640493.85327634,  6996019.10910718,
        6676046.37885942,  5424564.76186838,  6676046.37885942,
        9582971.61654306,  5637879.91536688, 11654425.2587511 ,
        6604941.32769325,  7067613.59470062,  5246802.13395296,
        7804085.7808911 ,  7067124.16027335,  5895384.63076716,
        9831839.29562465, 13241053.27653736,  8552952.75494193,
        6604941.32769325,  6711598.9044425 ,  8454932.21451138,
        5939574.19266894,  6853809.00677484, 10194742.03127642,
        6782703.95560867, 12952105.19826055,  9745724.10862716,
        9567472.04628447,  5246802.13395296,  5637879.91536688,
       26903467.72825824,  6552604.16390006,  6676046.37885942,
        6031221.63358687,  5939574.19266894,  8176885.10935933,
        6569388.80211017,  5460117.28745146,  5424564.76186838,
        9582971.61654306,  5246802.13395296,  9334103.93746147,
        8588505.28052502,  5317907.18511913,  5708984.96653305,
        5575411.90051941,  6782703.95560867,  8286811.00322296,
        7780438.60874109,  6711598.9044425 ,  8668247.36800988,
       11275252.83077025,  6640493.85327634, 11848972.52487663,
        7638228.50640876,  6676046.37885942,  6102326.68475304,
        7280928.74819913,  9254361.84997661,  8996367.70014905,
        5317907.18511913,  7496018.40407642,  5531222.33861763,
       10390548.85389971, 19268010.15861906,  5708984.96653305,
        7424913.35291025,  5389012.23628529, 15604070.34499317,
        8925262.64898288,  7318255.776161  ,  5424564.76186838,
        5317907.18511913,  8260384.94838585,  8854157.59781672,
        8747989.45549474,  8925752.08341016,  5173433.14598072,
        8552952.75494193,  9334103.93746147,  7353808.30174408,
        7531570.92965951,  5282354.65953604,  6640493.85327634])
























In [73]:



     


#y_test containing the correct spending cost/habit of tourist
y_test



     






















    
    
Out[73]:








4284     130000.0
3401    8453250.0
3204    7293000.0
3362     497250.0
3822    6132750.0
          ...    
2147    6298500.0
1418    3480750.0
3654    8619000.0
3137     600000.0
2253     497250.0
Name: total_cost, Length: 480, dtype: float64
























In [74]:



     


#comparing y_test to the prediction to find the residuals
plt.scatter(y_test, prediction/10**6)



     






















    
    
Out[74]:








<matplotlib.collections.PathCollection at 0x25b41197af0>










    
    































In [75]:



     


#plotting histogram histogram distribution of the residuals
sns.distplot(y_test- prediction/10**6)



     






















    
    
Out[75]:








<AxesSubplot:xlabel='total_cost', ylabel='Density'>










    
    



































Evaluating the model with loss functions so as to minimise the errors¶


















In [76]:



     


#looking at the linear evaluation metrics we get:
from sklearn import metrics



     



















In [77]:



     


#average error of the model
metrics.mean_absolute_error(y_test, prediction)



     






















    
    
Out[77]:








7232385.834148327
























In [78]:



     


#checking for larger error by squaring
metrics.mean_squared_error(y_test, prediction)



     






















    
    
Out[78]:








121440898468113.7
























In [79]:



     


#RMSE
np.sqrt(metrics.mean_squared_error(y_test, prediction))



     






















    
    
Out[79]:








11020022.616497377
























In [80]:



     


r_squared=lm.score(x,y)



     



















In [81]:



     


print(r_squared)



     






















    
    






0.09682259179087571
























In [82]:



     


r_squared=lm.score(x_test, prediction)



     



















In [83]:



     


print(r_squared)



     






















    
    






1.0
























In [84]:



     


from sklearn.metrics import r2_score



     



















In [85]:



     


r2_score(y_test, prediction)



     






















    
    
Out[85]:








0.17874474458236633
























In [86]:



     


r2_score(y_test, prediction).dtype



     






















    
    
Out[86]:








dtype('float64')




























TESTING LINEAR MODEL ASSUMPTION¶


















In [87]:



     


from sklearn.svm import SVC



     



















In [88]:



     


tz=pd.read_csv('test .csv')



     



















In [89]:



     


tz



     






















    
    
Out[89]:











  
    

      
      ID
      country
      age_group
      travel_with
      total_female
      total_male
      purpose
      main_activity
      info_source
      tour_arrangement
      ...
      package_food
      package_transport_tz
      package_sightseeing
      package_guided_tour
      package_insurance
      night_mainland
      night_zanzibar
      payment_mode
      first_trip_tz
      most_impressing
    

  
  
    

      0
      tour_1
      AUSTRALIA
      45-64
      Spouse
      1.0
      1.0
      Leisure and Holidays
      Wildlife tourism
      Travel, agent, tour operator
      Package Tour
      ...
      Yes
      Yes
      Yes
      Yes
      Yes
      10
      3
      Cash
      Yes
      Wildlife
    

    

      1
      tour_100
      SOUTH AFRICA
      25-44
      Friends/Relatives
      0.0
      4.0
      Business
      Wildlife tourism
      Tanzania Mission Abroad
      Package Tour
      ...
      No
      No
      No
      No
      No
      13
      0
      Cash
      No
      Wonderful Country, Landscape, Nature
    

    

      2
      tour_1001
      GERMANY
      25-44
      Friends/Relatives
      3.0
      0.0
      Leisure and Holidays
      Beach tourism
      Friends, relatives
      Independent
      ...
      No
      No
      No
      No
      No
      7
      14
      Cash
      No
      No comments
    

    

      3
      tour_1006
      CANADA
      24-Jan
      Friends/Relatives
      2.0
      0.0
      Leisure and Holidays
      Cultural tourism
      others
      Independent
      ...
      No
      No
      No
      No
      No
      0
      4
      Cash
      Yes
      Friendly People
    

    

      4
      tour_1009
      UNITED KINGDOM
      45-64
      Friends/Relatives
      2.0
      2.0
      Leisure and Holidays
      Wildlife tourism
      Friends, relatives
      Package Tour
      ...
      Yes
      Yes
      No
      No
      No
      10
      0
      Cash
      Yes
      Friendly People
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      1596
      tour_988
      UNITED STATES OF AMERICA
      25-44
      NaN
      0.0
      1.0
      Meetings and Conference
      Mountain climbing
      Newspaper, magazines,brochures
      Independent
      ...
      No
      No
      No
      No
      No
      1
      0
      Cash
      No
      NaN
    

    

      1597
      tour_990
      ITALY
      45-64
      Spouse and Children
      3.0
      1.0
      Leisure and Holidays
      Wildlife tourism
      Friends, relatives
      Package Tour
      ...
      Yes
      Yes
      Yes
      No
      No
      10
      5
      Other
      Yes
      Wildlife
    

    

      1598
      tour_992
      FINLAND
      25-44
      Alone
      0.0
      1.0
      Meetings and Conference
      Mountain climbing
      Friends, relatives
      Independent
      ...
      No
      No
      No
      No
      No
      6
      0
      Cash
      Yes
      No comments
    

    

      1599
      tour_996
      SOUTH AFRICA
      24-Jan
      Alone
      0.0
      1.0
      Business
      Beach tourism
      Friends, relatives
      Independent
      ...
      No
      No
      No
      No
      No
      4
      0
      Cash
      Yes
      Wildlife
    

    

      1600
      tour_998
      SOUTH AFRICA
      25-44
      Spouse
      1.0
      1.0
      Leisure and Holidays
      Cultural tourism
      Radio, TV, Web
      Independent
      ...
      No
      No
      No
      No
      No
      9
      5
      Cash
      Yes
      Friendly People
    

  



1601 rows × 22 columns


























In [90]:



     


tz.corr()



     






















    
    
Out[90]:











  
    

      
      total_female
      total_male
      night_mainland
      night_zanzibar
    

  
  
    

      total_female
      1.000000
      0.288933
      0.015265
      0.078020
    

    

      total_male
      0.288933
      1.000000
      -0.035880
      0.020622
    

    

      night_mainland
      0.015265
      -0.035880
      1.000000
      0.516262
    

    

      night_zanzibar
      0.078020
      0.020622
      0.516262
      1.000000
    

  



























In [91]:



     


sns.heatmap(tz.corr(), annot=True)



     






















    
    
Out[91]:








<AxesSubplot:>










    
    































In [92]:



     


lm.fit(x,y)



     






















    
    
Out[92]:








LinearRegression()
























In [93]:



     


y_pred=lm.predict(x_test)



     



















In [94]:



     


y_pred



     






















    
    
Out[94]:








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        7472629.67506169,  5048741.49619351,  6601049.11848093])
























In [95]:



     


#evaluate accuracy
from sklearn.metrics import accuracy_score
accuracy=lm.score(x_test, y_pred)
accuracy



     






















    
    
Out[95]:








1.0




























the result above reveals that after testing our model using a different dataset, our model accurately predicted the spending behaviour of the tourists considering the correlation values and put side by side the actual cost and the predicted values.¶


















In [98]:



     


x_test



     






















    
    
Out[98]:











  
    

      
      total_female
      total_male
      night_mainland
      night_zanzibar
    

  
  
    

      4284
      0.0
      1.0
      3.0
      0.0
    

    

      3401
      0.0
      1.0
      11.0
      0.0
    

    

      3204
      1.0
      1.0
      0.0
      7.0
    

    

      3362
      0.0
      1.0
      14.0
      0.0
    

    

      3822
      0.0
      1.0
      5.0
      0.0
    

    

      ...
      ...
      ...
      ...
      ...
    

    

      2147
      1.0
      1.0
      0.0
      7.0
    

    

      1418
      1.0
      1.0
      2.0
      0.0
    

    

      3654
      1.0
      1.0
      7.0
      0.0
    

    

      3137
      0.0
      1.0
      2.0
      0.0
    

    

      2253
      1.0
      0.0
      3.0
      0.0
    

  



480 rows × 4 columns






























The regression model is best fit for the data. To be precise, linear regression finds the smallest sum of squared residuals that is possible for the dataset. thi means that, if the difference between the observations and the predicted values are small and unbiased the model is fit for prediction.¶


















In [ ]: